Cell Imaging Systems and Methods

ABSTRACT

Disclosed herein are systems and methods for imaging cells. Quantitative phase imaging uses variations in the index of refraction of a sample as a source of endogenous contrast, providing label-free information of sub-cellular structures and allowing for the reconstruction of valuable biophysical parameters, such as cell dry-mass at femtogram scales, mass transport, and sample thickness and fluctuations at nanometer scales. As a result, QPI has become a valuable tool in biology and medicine. However, QPI has suffered from the need for trans-illumination through relatively thin objects in order to gain access to the forward-scattered field, which carries crucial low spatial frequency information of a sample and avoid contributions from multiple scattered light or out-of-focus planes. The disclosed methods and systems can provide for reconstruction of QPI and corresponding analysis for imaging samples of cells in thick samples using an epi-illumination configuration.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit, under 35 U.S.C. § 119(e), of U.S.Provisional Patent Application No. 62/648,180, filed 26 Mar. 2018, theentire contents and substance of which is incorporated herein byreference in its entirety as if fully set forth below.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to cell imaging systems andmethods. Particularly, embodiments of the present disclosure relate tomulti-wavelength and/or quantitative oblique back-illuminationmicroscopy.

BACKGROUND

Quantitative Phase Imaging (“QPI”) uses variations in the index ofrefraction of a sample as a source of endogenous contrast, providinglabel-free information of sub-cellular structures and allowing for thereconstruction of valuable biophysical parameters, such as cell dry-massat femtogram scales, mass transport, and sample thickness andfluctuations at nanometer scales. As a result, QPI has become a valuabletool in biology and medicine, with a growing set of applications infields like oncology, hematology, pathology, immunology, developmentalbiology, and neuroscience. However, QPI has suffered from the need fortrans-illumination through relatively thin objects in order to gainaccess to the forward-scattered field, which carries crucial low spatialfrequency information of a sample and avoid contributions from multiplescattered light or out-of-focus planes. This restriction has severelylimited the biological applicability of phase imaging to mostlythinly-sliced histological tissue, cultured cells, or thin transparentsamples in-vitro.

Methods for QPI typically involve interfering beams of a coherentsource, but phase contrast itself can be produced simply with partiallycoherent asymmetric illumination in a typical wide-field microscope,without interferometry. Images produced from incoherent or partiallycoherent light sources have the advantage of increased resolution and alack of noise from speckle or other coherent artifacts. Improved methodsof QPI have potential to greatly expand the design space andcapabilities of certain practices, such as stem cell characterization,stem cell therapy, transplant tissue characterization, white blood cellcount, storage lesions, endoscopy, in-vivo imaging and the like.

What is needed, therefore, is an improved cell imaging technique toenable a rich level of quantitative detail in thick scattering samplessimilar to that achieved with thin forward-illuminating samples.Embodiments of the present disclosure address this need as well as otherneeds that will become apparent upon reading the description below inconjunction with the drawings.

BRIEF SUMMARY OF THE INVENTION

The present invention relates to cell imaging systems and methods. Anexemplary embodiment of the present invention provides a method ofimaging cell samples, comprising: obtaining a quantitative phase imageof a plurality of cells; obtaining a distribution of light frequencyabsorption data for the plurality of cells; cross-correlating a samplemodel of a desired cell with the quantitative phase image to compareeach cell from the plurality of cells with the sample model of thedesired cell; indicating at least one cell from the plurality of cellssimilar to the sample model as a first desired cell candidate;indicating at least one cell from the plurality of cells having a lightfrequency absorption outside of a threshold standard deviation from theplurality of cells as a second desired cell candidate; and determining,based on the quantitative phase image and the distribution of lightfrequency absorption data, if the first desired cell candidate and thesecond desired cell candidate are the same cell.

In any of the embodiments disclosed herein, obtaining a distribution oflight frequency absorption data can comprise: illuminating the pluralityof cells with light at a first frequency; illuminating the plurality ofcells with light at a second frequency; and receiving two or moreilluminated images of the plurality of cells.

In any of the embodiments disclosed herein, the method can furthercomprise comparing a value of light absorbed at the first frequency tolight absorbed at the second frequency for each cell from the pluralityof cells.

In any of the embodiments disclosed herein, the method can furthercomprise constructing a phase gradient image by subtracting anilluminated image at the second frequency from an illuminated image atthe first frequency.

In any of the embodiments disclosed herein, the method can furthercomprise constructing an absorption contrast image by adding the two ormore illuminated images together.

In any of the embodiments disclosed herein, the illuminating cancomprise: emitting light at the first frequency from a first pair oflight sources; and emitting light at the second frequency from a secondpair of light sources. The first and second pairs of light sources andan objective lens can be on a same side of the plurality of cells. Thefirst and second pairs of light sources can be configured to transmitlight obliquely to the plurality of cells.

In any of the embodiments disclosed herein, the first and second lightsources can comprise two or more light-emitting devices.

In any of the embodiments disclosed herein, the two or morelight-emitting devices can comprise light-emitting diodes (LEDs).

In any of the embodiments disclosed herein, the two or morelight-emitting devices comprise fiber optic cables.

In any of the embodiments disclosed herein, a first and a secondlight-emitting device can be positioned flanking the objective, suchthat each of the first and second light sources can comprise a first anda second light-emitting device on either side of the objective.

In any of the embodiments disclosed herein, the first and the secondlight-emitting devices on either side of the objective can form anorthogonal angle with each other, such that each of the first and secondlight sources comprise a first and a second light-emitting device oneither side of the objective and forming an orthogonal angle.

In any of the embodiments disclosed herein, the method can furthercomprise labelling the cell corresponding to the first and the seconddesired cell candidate as a desired cell, responsive to determining thatthe first and second desired cell candidates are the same cell.

In any of the embodiments disclosed herein, the method can furthercomprise labelling the cell corresponding to the first and the seconddesired cell candidate as a false positive, responsive to determiningthat the first and second desired cell candidates are not the same cell.

In any of the embodiments disclosed herein, the indicating at least onecell from the plurality of cells having a light frequency absorptionoutside of a threshold standard deviation from the plurality of cellscan comprise: illuminating the plurality of cells with light at a firstfrequency; illuminating the plurality of cells with light at a secondfrequency; calculating the ratio of light absorbed at the firstfrequency to light absorbed at the second frequency for each cell fromthe plurality of cells; and determining which cells from the pluralityof cells have a ratio of light absorbed outside a threshold standarddeviation value from the plurality of cells.

In any of the embodiments disclosed herein, the desired cell can be awhite blood cell.

In any of the embodiments disclosed herein, the plurality of cells cancomprise blood cells.

In any of the embodiments disclosed herein, the cells can be obtainedfrom any organ or organoid belonging to a living organism.

Another embodiment provides a method of imaging blood comprising:illuminating a plurality of blood cells with light at a first frequencyfrom a first light source; illuminating the plurality of blood cellswith light at a second frequency from a second light source; receivingtwo or more illuminated images of the plurality of blood cells at anobjective; constructing a quantitative phase image from the two or moreilluminated images with epi illumination by including the two or moreilluminated images together; cross-correlating a sample model of a whiteblood cell with the quantitative phase image to compare each cell fromthe plurality of blood cells with the white blood cell; indicating atleast one cell from the plurality of blood cells matches the samplemodel as a first white blood cell candidate; indicating at least onecell from the plurality of blood cells having a light frequencyabsorption ratio outside of a threshold standard deviation from theplurality of cells as a second white blood cell candidate; determining,based on the quantitative phase image and the distribution of lightfrequency absorption data, if the first white blood cell candidate andthe second white blood cell candidate are the same cell; and labellingthe cell corresponding to the first and the second white blood cellcandidate as a white blood cell, responsive to determining that thefirst and second white blood cell candidates are the same cell.

Another embodiment provides a method of imaging cell samples,comprising: cross-correlating a sample model of a desired cell with aquantitative phase image to compare each cell from a plurality of cellswith the desired cell; indicating at least one cell from the pluralityof cells similar to the sample model as a first desired cell candidate;indicating at least one cell from the plurality of cells having a lightfrequency absorption outside of a threshold standard deviation from theplurality of cells as a second desired cell candidate; and determining,based on the quantitative phase image and the distribution of lightfrequency absorption data, if the first desired cell candidate and thesecond desired cell candidate are the same cell.

In any of the embodiments disclosed herein, the method can furthercomprise: obtaining a distribution of light frequency absorption databy: illuminating the plurality of cells with light at a first frequency;illuminating the plurality of cells with light at a second frequency;and receiving two or more illuminated image of the plurality of cells atan objective.

In any of the embodiments disclosed herein, the method can furthercomprise calculating a ratio of light absorbed at the first frequency tolight absorbed at the second frequency for each cell from the pluralityof cells.

Another embodiment provides a system for the imaging of cells. Thesystem comprises a first and a second light source, an objectiveimage-capturing device, a display, a processor, and memory. Each of thefirst and second light sources can comprise two or more light-emittingdevices. The memory can store instructions that, when executed by theprocessor, cause the system to: receive imaging data from the objectiveimage-capturing device, the imaging data comprising light frequencyabsorption data for a plurality of cells; construct, using the lightfrequency absorption data, a quantitative phase image of the pluralityof cells; cross-correlate a sample model of a desired cell with thequantitative phase image to compare each cell from the plurality ofcells with the sample model of the desired cell; indicate at least onecell from the plurality of cells similar to the sample model as a firstdesired cell candidate; indicate at least one cell from the plurality ofcells having a light frequency absorption outside of a thresholdstandard deviation from the plurality of cells as a second desired cellcandidate; and determine, based on the quantitative phase image and thedistribution of light frequency absorption data, if the first desiredcell candidate and the second desired cell candidate are the same cell.

In any of the embodiments disclosed herein, the at least one memory canfurther comprise instructions, that when executed by the processor,cause the system to: illuminate the plurality of cells with light fromthe first light source at a first frequency; illuminate the plurality ofcells with light from the second light source at a second frequency;receive two or more illuminated images of the plurality of cells at anobjective; and receive light frequency absorption data from theobjective imaging device.

In any of the embodiments disclosed herein, the at least one memory canfurther comprise instructions, that when executed by the processor,cause the system to calculate a ratio of light absorbed at the firstfrequency to light absorbed at the second frequency for each cell fromthe plurality of cells.

In any of the embodiments disclosed herein, the at least one memory canfurther comprise instructions, that when executed by the processor,cause the system to construct a phase gradient image by subtracting anilluminated image at the second frequency from an illuminated image atthe first frequency.

In any of the embodiments disclosed herein, the at least one memory canfurther comprise instructions, that when executed by the processor,cause the system to construct an absorption contrast image by adding thetwo or more illuminated images together.

In any of the embodiments disclosed herein, the first and second lightsources can be on the same side of the plurality of cells as theobjective.

In any of the embodiments disclosed herein, the first and second lightsources can be configured to transmit light obliquely to the pluralityof cells.

In any of the embodiments disclosed herein, the at least one memory canfurther comprise instructions, that when executed by the processor,cause the system to label the cell corresponding to the first and thesecond desired cell candidate as a desired cell, responsive todetermining that the first and second desired cell candidates are thesame cell.

In any of the embodiments disclosed herein, the at least one memory canfurther comprise instructions, that when executed by the processor,cause the system to transmit the imaging data to the display.

In any of the embodiments disclosed herein, the at least one memoryfurther comprises instructions, that when executed by the processor,cause the system to transmit at least one image from the two or moreilluminated images of the plurality of cells to the display.

In any of the embodiments disclosed herein, the at least one image cancomprise the desired cell labels applied by the system.

In any of the embodiments disclosed herein, the at least one memory canfurther comprise instructions, that when executed by the processor,cause the system to label the cell corresponding to the first and thesecond desired cell candidate as a false positive, responsive todetermining that the first and second desired cell candidates are notthe same cell.

In any of the embodiments disclosed herein, the at least one memory canfurther comprise instructions, that when executed by the processor,cause the system to: calculate the ratio of light absorbed at the firstfrequency to light absorbed at the second frequency for each cell fromthe plurality of cells; and determine which cells from the plurality ofcells have a ratio of light absorbed outside a threshold standarddeviation value from the plurality of cells.

In any of the embodiments disclosed herein, the desired cell can be awhite blood cell.

In any of the embodiments disclosed herein, the plurality of cells cancomprise blood cells.

In any of the embodiments disclosed herein, the blood cells can beobtained from any organ or organoid belonging to a living organism.

Another embodiment provides a system for imaging cells comprising afirst and a second light source, an objective image-capturing device, aplurality of cells, a process, and memory. Each of the first and secondlight source can comprise two or more light-emitting devices and can beconfigured to illuminate a plurality of cells. The memory can storeinstructions that, when executed by the processor, cause the system to:receive imaging data from the objective image-capturing device, theimaging data comprising light frequency absorption data for a pluralityof cells; construct, using the light frequency absorption data, aquantitative phase image of the plurality of cells; cross-correlate asample model of a desired cell with the quantitative phase image tocompare each cell from the plurality of cells with the desired cell;indicate at least one cell from the plurality of cells similar to thesample model as a first desired cell candidate; indicate at least onecell from the plurality of cells having a light frequency absorptionoutside of a threshold standard deviation from the plurality of cells asa second desired cell candidate. The first and second light sources canbe on the same side of the plurality of cells as the objective and areconfigured to transmit light obliquely to the plurality of cells. Afirst and a second light-emitting device from the two or morelight-emitting devices can be positioned flanking the objective, suchthat each of the first and second light sources comprise a first and asecond light-emitting device on either side of the objective. The firstand the second light-emitting devices on either side of the objectivecan form an orthogonal angle with each other, such that each of thefirst and second light sources comprise a first and a secondlight-emitting device on either side of the objective and forming anorthogonal angle.

These and other aspects of the present invention are described in theDetailed Description of the Invention below and the accompanyingfigures. Other aspects and features of embodiments of the presentinvention will become apparent to those of ordinary skill in the artupon reviewing the following description of specific, exemplaryembodiments of the present invention in concert with the figures. Whilefeatures of the present invention may be discussed relative to certainembodiments and figures, all embodiments of the present invention caninclude one or more of the features discussed herein. Further, while oneor more embodiments may be discussed as having certain advantageousfeatures, one or more of such features may also be used with the variousembodiments of the invention discussed herein. In similar fashion, whileexemplary embodiments may be discussed below as device, system, ormethod embodiments, it is to be understood that such exemplaryembodiments can be implemented in various devices, systems, and methodsof the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate multiple embodiments of thepresently disclosed subject matter and serve to explain the principlesof the presently disclosed subject matter. The drawings are not intendedto limit the scope of the presently disclosed subject matter in anymanner.

FIG. 1a illustrates an exemplary embodiment of a system for cellimaging;

FIG. 1b illustrates an exemplary embodiment of a system for cellimaging;

FIG. 2 is a flowchart of an exemplary method for imaging cells;

FIG. 3 is a flowchart of an exemplary method for imaging cells;

FIG. 4 is a flowchart of an exemplary method for imaging cells;

FIG. 5 shows images produced by exemplary embodiments of a system forcell imaging and the methods of processing thereof;

FIG. 6a shows an image produced by exemplary embodiments of a system forcell imaging and the methods of processing thereof;

FIG. 6b shows a plot of transmission of light at a first and secondfrequency for a plurality of cells;

FIG. 7 shows reconstructed images of a plurality of cells obtained fromthe presently disclosed system and method;

FIG. 8 shows reconstructed images of a plurality of cells obtained fromthe presently disclosed system and method;

FIG. 9 shows reconstructed images of a plurality of cells obtained fromthe presently disclosed system and method; and

FIG. 10 is a flowchart of an exemplary method for the construction of aquantitative phase image for use in the presently disclosed method forimaging cells.

DETAILED DESCRIPTION

Although certain embodiments of the disclosure are explained in detail,it is to be understood that other embodiments are contemplated.Accordingly, it is not intended that the disclosure is limited in itsscope to the details of construction and arrangement of components setforth in the following description or illustrated in the drawings. Otherembodiments of the disclosure are capable of being practiced or carriedout in various ways. Also, in describing the embodiments, specificterminology will be resorted to for the sake of clarity. It is intendedthat each term contemplates its broadest meaning as understood by thoseskilled in the art and includes all technical equivalents which operatein a similar manner to accomplish a similar purpose.

Herein, the use of terms such as “having,” “has,” “including,” or“includes” are open-ended and are intended to have the same meaning asterms such as “comprising” or “comprises” and not preclude the presenceof other structure, material, or acts. Similarly, though the use ofterms such as “can” or “may” are intended to be open-ended and toreflect that structure, material, or acts are not necessary, the failureto use such terms is not intended to reflect that structure, material,or acts are essential. To the extent that structure, material, or actsare presently considered to be essential, they are identified as such.

By “comprising” or “containing” or “including” is meant that at leastthe named compound, element, particle, or method step is present in thecomposition or article or method, but does not exclude the presence ofother compounds, materials, particles, method steps, even if the othersuch compounds, material, particles, method steps have the same functionas what is named.

It is also to be understood that the mention of one or more method stepsdoes not preclude the presence of additional method steps or interveningmethod steps between those steps expressly identified.

The components described hereinafter as making up various elements ofthe disclosure are intended to be illustrative and not restrictive. Manysuitable components that would perform the same or similar functions asthe components described herein are intended to be embraced within thescope of the disclosure. Such other components not described herein caninclude, but are not limited to, for example, similar components thatare developed after development of the presently disclosed subjectmatter.

As described above, a problem with current quantitative phase imaging(QPI) techniques is the need for trans- or forward-illumination andthus, the need for a thin sample. This restriction has severely limitedthe biological applicability of phase imaging to mostly thinly-slicedtissue samples, cultured cells, or other transparent samples. With thedesign capacity for QPI techniques expanding in areas such as oncology,hematology, pathology, immunology, developmental biology, andneuroscience, improved cell imaging methods are desirable.

The field of incoherent or partially coherent light sources have beenexplored. Images produced from incoherent or partially coherent lightsources have the advantage of increased resolution and a lack of noisefrom speckle or other coherent artifacts. The phase contrast producedfrom asymmetric illumination can be used to recover quantitative phasewith a complete linearized model of the imaging system via a regularizeddeconvolution with the optical transfer function of the microscope. Thistype of phase reconstruction method does not suffer from phase wrappingartifacts and has recently been widely adopted for QPI (including 3Dtomographic phase reconstruction of thin samples) using partiallycoherent structured illumination sources such as LED arrays and modifiedpupils.

Using principles of asymmetric illumination to recover quantitativephase, transmissive QPI has been transformed into an epi-mode imagingmodality with tomographic capabilities. To achieve this, a modifiedversion of an illumination scheme known as oblique back-illuminationmicroscopy (OBM) can be used, which produces a virtual light sourcewithin a thick sample, via multiple scattering, that emulates atransmission geometry with oblique illumination. By subtracting twoimages acquired with opposite oblique illumination angles, this strategyeffectively removes contributions from out-of-focus planes, and yieldshigh-resolution, tomographic differential phase contrast in thickspecimens. Then, the multiple scattering process used for illuminationto arrive at the ensemble average angular distribution of lightapproaching the target can be modeled, and ultimately converted into atransfer function of the system. This allows for use of regularizeddeconvolution methods to recover quantitative phase. This approach,termed quantitative oblique back-illumination microscopy (qOBM),incorporates the trappings of QPI into a versatile epi-configuration,allowing non-invasive, label-free, real-time quantitative imaging inmedia that are otherwise out of the reach of previous QPI technologies.With qOBM, quantitative phase imaging can now be extended to many moreareas of biomedicine. Disclosed herein are the system and theoreticalframework of the aforementioned techniques.

Disclosed herein is a method for imaging a plurality of cells. Themethod can comprise imaging and deconvolution to obtain a QPI of asample. The QPI can be obtained in a number of ways, but a method ofinterest utilizes oblique back-illumination microscopy. In other words,light sources on the same side of the sample as the imaging deviceilluminate the sample at multiple oblique angles. Such illumination canproduce a virtual light source within a thick sample, via multiplescattering, that emulates a transmission geometry with obliqueillumination. An image of the sample can be obtained by subtracting twoor more images acquired with opposing oblique illumination angles. Forexample, two light sources can be placed to form an orthogonal anglewith one another while remaining oblique to the sample. The subtractingeffectively removes light transmission and absorption from out-of-focusplanes and yields high-resolution differential phase contrast images ofthe area of the thick sample in question. In other words, theaforementioned strategy can image a thick sample to produce an effectivecross-sectional image of the sample at a desired area. The differentialphase contrast image is then processed using mathematical formulas, suchas the multiple scattering process used for illumination, to obtain theaverage angular distribution of light received by the imaging device.This device can be used to obtain the transfer function of the system.That is, spatial data relating to locations in the spatial domain can betransferred to become frequency data in the frequency domain, asfrequency data is desirable in some applications where the cells ofinterest absorb light at differing frequencies. From this transferfunction, the differential phase contrast image can be deconvoluted torecover a quantitative phase image (QPI). Such an embodiment can providenon-invasive, label-free, and real-time quantitative imaging in mediathat are otherwise out of the reach of currently known QPI technologies.

Disclosed herein are systems for imaging cells. Embodiments of thepresent disclosure can provide a system comprising a plurality of lightsources (e.g., one or more, two or more, three or more, four or more,etc.), an objective image-capturing device (such as a camera), adisplay, a plurality of storage devices/memory (e.g., one or more, twoor more, three or more), and a plurality of processors (e.g., one ormore, two or more, three or more). For instance, the system can comprisefour or more light sources in the form of light-emitting diodes (LEDs)or optical fibers, and an objective camera configured to illuminatesamples and capture images of samples. The imaging and illumination datacan be transmitted to one or more storage devices at a computer. The oneor more processors at the computer can be configured to process theimages and/or the illumination data and can contain respectiveinstructions for the processing of the data. The display can also be incommunication with the computer and can receive images and/or data todisplay to the user from the storage devices or the processors.

For example, FIG. 1a provides an exemplary embodiment of a system forimaging cells. In some embodiments, the system consists of a traditionalmicroscope with epi-illumination emanating from two pairs of opticalfibers positioned around the objective housing. As used herein, the term“epi-illumination” refers to illumination techniques wherein theillumination light source emits from the same side of the sample as theobjective image-capturing device. The fibers from each pair can beplaced diametrically opposite from one another as shown in FIG. 1b .When light from an LED light source is deployed through one of thefibers, it produces trans-illumination at the focus of the microscopeobjective by way of multiple scattering. With an overall obliqueillumination at the focal plane, lateral variations in index ofrefraction redirect light toward or away from the acceptance angles ofthe objective's numerical aperture, producing phase contrast in observedintensity. As with oblique back-illumination microscopy (OBM),quantitative oblique back-illumination microscopy (qOBM) first generatesdifferential phase contrast by subtracting images taken with thediametrically opposed fibers. Again, the asymmetric illumination and thesubtraction process produce differential phase contrast; and, as out offocus contributions in either illumination image are the same, thesubtraction process rejects out of focus content, allowing fortomographic sectioning.

In some embodiments, the plurality of processors can containinstructions for processing the imaging and illumination data receivedfrom the objective image capturing device. Such data can be in the formof spatial location data illumination absorption data, illuminationtransmission data, frequency data, wavelength data, and the like. Insome embodiments, the plurality of processors (such as the one or moreprocessors) can be configured to process different images to constructphase images. As mentioned above, the one or more processors can containinstructions configured to construct a differential phase contrast image(DPC) and/or a quantitative phase image (QPI), as outlined generally inFIG. 10. Constructing such images would allow for greater analysis ofthe imaging data received from the sample.

In practice, differential phase contrast can be produced by firstnormalizing each intensity image by its overall variance, which removesany variations between the LED output. Then, images corresponding to thediametrically opposed sides are subtracted and normalized by their sum(i.e., the background intensity) to produce a differential phasecontrast image as shown in Eq. (1),

$\begin{matrix}{I_{DPC} = \frac{I_{L} - I_{R}}{I_{L} + I_{R}}} & (1)\end{matrix}$

where I_(L) and I_(R) represent the images with left and rightillumination, respectively. Conventional, absorption-based contrast canbe obtained by taking the sum of two opposite oblique illuminationimages.

In some embodiments, the one or more processors can be configured toconstruct a phase image of the sample based on the imaging datareceived. To extract quantitative phase from the collected differentialphase contrast image, a transfer function for the imaging system as awhole can be found. While the formula for image formation in amicroscope can be explained in terms of the propagation of mutualintensity, an equivalent description can be made in the context ofangular spectra, which is more natural in the context of the presentlydisclosed illumination scheme. Thus, the measured intensity can bedescribed by the illumination field E(x) (with Fourier pair E(u)), witha distribution of illumination angles over u, the 2-dimensional angularcoordinates, multiplied by the object transmittance function o(x). Thiscomplex distribution can then be then convolved with the pupil functionof the system, and the amplitude squared is taken to yield the observedintensity. This can be expressed succinctly as,

I(r)=|

⁻¹(ƒ)

{o(x)

⁻¹ {E(u)}}}|²  (2)

where

represents a 2D Fourier transform from spatial coordinates intospatial-frequency coordinates, x are the spatial coordinates at thefocal plane, P(ƒ) is the pupil function, either 0 or 1, in spatialfrequency coordinates f that correspond to physical coordinates at theback focal plane, and r represents spatial coordinates at the camera.The coordinates u and f are both spatial frequency units, which map tounits of propagation angle when scaled by a factor of λ. The coordinatesx and r are in units of distance and correspond when scaledappropriately for magnification.

If the light source is incoherent, then Eq. (2) can be expressed as,

$\begin{matrix}{{I(r)} = {\int{{S(u)}{{\int{{{P(f)}\lbrack {\int{{o(x)}e^{i2{{\pi {({f - u})}} \cdot x}}d^{2}x}} \rbrack}e^{i2{\pi {({f \cdot r})}}}d^{2}f}}}^{2}d^{2}u}}} & (3)\end{matrix}$

where S(u)=|E(u)|² is the corresponding angular intensity distributionfrom the scattering medium. In this form, the terms inside the modulusbracket indicate the equivalent intensity of an image formed whenilluminated from a coherent source with a single incident angle u, andthe outer integral indicates that the image formed from partial or fullyincoherent illumination is the incoherent sum of images formed fromcontributing individual coherent plane waves.

Further, Eq. (3) demonstrates that the angular distribution of thesource illumination intensity around the target gives sufficientinformation to produce a transfer function. In other words, the extentof spatial coherence of the microscope is fully determined by thebreadth of the illumination intensity in angle space. This is beneficialfor qOBM, as this quantity is readily available from photon transportsimulation.

To extract the overall system transfer function from Eq. (3), someembodiments can expand the quantity within the modulus brackets usingthe identity |∫ƒ(m) dm|²=∫∫ƒ(m)ƒ*(n) dm dn. Substituting the variablem=f−u, and introducing the integration variable n, one of ordinary skillin the art can transform o(x) to O(m) and O(n), and Eq. (3) becomes,

I(r)=∫∫O(m)O*(n)C(m,n)exp(i2π(r·(m−n)))d ² md ² n  (4)

where, O(m) is the Fourier transform of the target object function o(r),m and n are variables of integration in the spatial frequency space, andC is the transfer function of the microscope for a single image:

C(m,n)=∫S(u)P(m+u)P*(n+u)d ² u  (5)

Here P is the pupil function of the system in u, the coordinates in theback focal plane of the microscope, and S(u) represents the powerspectrum in angular frequency of the illuminating light, or,proportionately, in illumination angle at the object. Equations (4) and(5) recapitulate the four-dimensional transfer function for a partiallycoherent microscope known in the art and in the literature from theHopkins equation for mutual coherence. This demonstrates that theangular intensity distribution can provide sufficient information todetermine the coherence of a system with an incoherent source,validating the use of illumination intensity angular spectra as astarting point for transfer function generation.

Equation (4) can be represented as a four-dimensional convolution withcomplex conjugate terms, a source of nonlinearity. This can belinearized with the assumption of a weak object. A thick sample mayitself not be weakly scattering but scattering from outside of the focalplane can contribute to the angular intensity spectrum, so local phaseeffects outside of this region may not contribute to the image. Thescattering of the object within the Rayleigh range is all thatcontributes to the reconstruction, and this can be said to be weak:

o(x)=exp(−μ(x)+iϕ(x))≈1−μ(x)+iϕ(x)  (6)

where μ(x) and ϕ(x) are the (real valued) absorption and phase functionsof the object. This means that the object function in the spatialfrequency space is given by:

O(m)=δ(m)−M(m)+iϕ(m)  (7)

where M and Φ are Fourier transforms of μ and ϕ, respectively. Expandingthe product of the objects from Eq. (4), gives:

O(m)O*(n)=δ(m)δ(n)−[M(m)δ(n)+M*(n)δ(m)]+i[ϕ(m)δ(n)−ϕ*(n)δ(m)]+ . . .  (8)

where the ellipses indicate cross terms that can be neglected as theyare assumed to be small components of the weak phase. The deltafunctions in Eq. (8) serve to reduce the dimensions of thefour-dimensional convolution to two dimensions when substituted in Eq.(4).

From the two-dimensional result, an optical transfer function can bederived to convert the DPC image to a phase image. The image that isproduced in this way has phase gradient contrast, and the equivalent 2Doptical phase transfer function is therefore given by,

C _(Δ)(m)=[∫S(u)P(m+u)P*(u)d ² u−∫S(u′)P(m+u)P*(u)d ² u]  (9)

where u′ represents the coordinates in the back focal plane inverted inthe shear direction: u′=[−u₁, u₂], and the delta functions in Eq. (8)have allowed the setting of n=0 in Eq. (5). This transfer function isreal and odd, therefore the point spread function given by its Fouriertransform (c_(δ)(r)) is purely imaginary. Hermitian symmetry ensuresthat an even source distribution gives rise to images that displayabsorption information, while an odd source distribution (synthesizedhere with the subtraction operation in Eq. (1)) gives phase information.Finally, the sum in the denominator of Eq. (1) normalizes the image byC(0, 0), a real-valued constant background term, and the DPC image cannow be expressed as,

$\begin{matrix}\begin{matrix}{{I_{DPC}(r)} = {\mathcal{F}^{- 1}\{ {{\frac{C_{\Delta}(m)}{C( {0,0} )} \cdot i}\; {\varphi (m)}} \}}} \\{= \frac{{Im}\{ {c_{\delta}(r)} \}*{\varphi (r)}}{C( {0,0} )}}\end{matrix} & \begin{matrix}(10) \\\begin{matrix}\; \\(11)\end{matrix}\end{matrix}\end{matrix}$

which directly yields access to the phase of the object.

In some embodiments, the one or more processors can be furtherconfigured to construct or deconvolute a QPI from the DPC image. Fromthe distribution obtained from the photon transport simulation for asingle LED source, an optical transfer function can be produced for thedifferential phase contrast (DPC) image formed by the microscope usingEq. (9), which can then be applied to recover the object function (Eq.(6)) with a deconvolution.

The formalism presented above only treats two sources to estimate thephase from a single DPC image which only carries information aboutrefractive index variations along one direction (the direction betweenthe two sources). Using a single shear direction to recover phase canlead to streak artifacts. To produce a fully quantitative phase image,the second pair of illumination sources is utilized, positionedorthogonally in horizontal angle to the first pair as shown in FIG. 1b .Then, the image of the ground truth object is reproduced with Tikhonovregularized deconvolution according to:

$\begin{matrix}{\varphi = {\mathcal{F}^{- 1}\{ \frac{\Sigma_{k}\frac{\lambda_{0}}{\lambda_{k}}I_{DPC}^{k}C_{DPC}^{*}}{{\Sigma_{k}{C_{DPC}}^{2}} + \alpha} \}}} & (12)\end{matrix}$

where

${C_{DPC} = \frac{iC_{\Delta}}{C( {0,0} )}},$

α is a regularization parameter, and the wavelength parameter λ_(k)allows the phase displacement to be normalized to the correspondingphase of a single wavelength λ₀ (e.g., green λ₀=530 nm). Here k=2,corresponding to the two orthogonal DPC images. The result is a 2Dquantitative tomographic phase image of an object embedded in ascattering medium. Combining the contributions from two orthogonaldimensions allows for features oriented in any direction to appear withequal contrast and produces an image that are both quantitative andsuperior in quality to those produced by either of the individual pairsof illuminations on their own.

The regularization parameter a can have a significant effect on thephase values in the images, so in order to ensure that an unbiasedmeasure of phase could be obtained, an algorithm to arrive at the valueautomatically can be implemented. Although it affects both image qualityand quantitative value, there is a theoretical optimal choice for a thatmaximizes smoothing of the image noise while minimizing the effect ofcausing a mismatch in the division operation in Eq. (12).

The regularization parameter was determined with generalized crossvalidation (GCV), as it requires no prior information about image ornoise power. The GCV estimate of α is given in linear algebra formalismby:

α=argmin{V(α)}  (13)

where

$\begin{matrix}{{V(\alpha)} = \frac{{( {{Af} - } )}^{2}}{{{Trace}\lbrack {I - {A(\alpha)}} \rbrack}^{2}}} & (14)\end{matrix}$

where A is a convolution matrix representation of the point spreadfunction of the system, ƒ is a solution for a given value of α, g is thecollected image data, I is the identity matrix, and A(α)=AA*(AA*+αI)⁻¹.Despite the recondite formalism, this approach has an intuitiveexplanation. The numerator in Eq. (14) represents the mean square errorbetween the captured phase gradient image and the reconstructed phasegradient image. This reconstructed phase gradient can be produced byfiltering the reconstructed quantitative phase object image through thesystem transfer function a second time, as if it were the originalobject being imaged. This reproduces a second phase gradient imagesimilar to the one originally captured, only distorted slightly byhaving been processed a second time. The more similar the originalcaptured phase gradient image is to the reprocessed one, the morefaithfully the deconvolution procedure inverts the transfer function ofthe system. Therefore, minimizing this difference reduces the errorintroduced by the regularization parameter. The denominator in Eq. (14)represents the square sum deviation from unity when the forward transferfunction of the system is deconvolved from itself, according to Eq.(12). This term mirrors the error in the numerator by analogy, but itserves to normalize the numerator by the amount of error theregularization parameter induces on the reprocessed transfer functionitself. Images captured of different scenes with different ambientillumination intensity may alter the minimum value found with thenumerator error term alone. The denominator term, then, ensures that theregularization parameter chosen is invariant with different imagesprocessed from the same modality. Because of this normalization term,this procedure can be performed once for a given imaging sample, ratherthan for each phase image. Alternatively, this procedure can beperformed more than once for a given imaging sample, depending ondesired resolution.

In some embodiments, a sample plurality of cells can be characterizedusing the presently disclosed imaging system. Embodiments of the presentdisclosure can provide a quantitative phase image (QPI) of a pluralityof cells. In some embodiments, a user of the system can analyze aplurality of cells for a desired cell. In such an embodiment, the systemcan be configured to receive a sample model of the desired cell from theuser and cross-correlate the sample model with the QPI to compare eachcell from the plurality of cells with the desired cell. The system canbe further configured to indicate at least one cell from the pluralityof cells is within a threshold similarity of, or similar to, the samplemodel. The system can then indicate that said at least one cell is apotential desired cell candidate, or a first desired cell candidateshould there be one or more desired cell candidates. The system canindicate additional cells from the plurality of cells as desired cellcandidates (e.g., a second desired cell candidate, a third desired cellcandidate, etc.) as shown in FIG. 6 a.

In some embodiments, light frequency absorption/transmission data can bereceived by the system from an objective image-capturing device. In someembodiments, the image-capturing device can be a camera, a flexible, ora rigid endoscope, or a fiber bundle to collect light frequency data.The light frequency data can comprise data regarding the frequencytransmission of a plurality of cells. Said data may contain adistribution of light absorption data. In some embodiments, the systemcan be configured to calculate a relative or absolute value of lightabsorbed at a first and second frequency and compare the twoabsorptions. In some embodiments, cells from the plurality of cellsfalling outside a threshold standard deviation from the plurality ofcells in the distribution can be indicated as desired cell candidates bythe system. In some embodiments, the threshold standard deviation can be1 or more (e.g., 1.5 or more, 2 or more, 2.5 or more, etc.). The systemcan then indicate that said at least one cell is a potential desiredcell candidate, or a first desired cell candidate should there be one ormore desired cell candidates. The system can indicate additional cellsfrom the plurality of cells as desired cell candidates (e.g., a seconddesired cell candidate, a third desired cell candidate, etc.). Anexemplary embodiment of a distribution of transmission data for aplurality of cells with indicated outliers is shown in FIG. 6 b.

The system can further be configured to determine if there exists anyoverlap between the desired cell candidates from the QPI and the desiredcell candidates from the absorption data. Should any cell from theplurality of cells be indicated as a desired cell by both the QPI andthe absorption data, the system can indicate that cell as a desiredcell. In some embodiments, the system can be configured to label thedesired cell as a desired cell. Alternatively, the system can beconfigured to label a desired cell candidate as a false positive whenthe candidate only meets one of the desired cell criteria of the QPI orabsorption data. Additionally, the system can provide a display of theimage to a user of the system on a display with the correspondinglabels.

Some embodiments of the present disclosure can provide a system forilluminating a sample to obtain images, imaging data, or light frequencyabsorption/transmission data. In some embodiments, the sample can beilluminated at for more frequencies (e.g., 2 or more, 3 or more, 4 ormore). In such an embodiment, the illumination can be provided by 1 ormore light sources (e.g., 2 or more, 3 or more, 4 or more, 5 or more, 6or more). In some embodiments, the light sources can be provided inpairs and can be provided in 1 or more pairs (e.g., 2 or more pairs, 3or more pairs, 4 or more pairs). In some embodiments, the light sourcescan be arranged such that an objective image capturing device is able tocapture an illuminated image through epi-illumination as defined herein.In some embodiments, the light sources can comprise 1 or morelight-emitting devices (e.g., 2 or more, 3 or more, 4 or more, 5 ormore, 6 or more), such as light-emitting diodes (LEDs), optical fibers,fiber optic cables, incandescent lamps, and the like. In someembodiments, light from the light-emitting devices can be emitted at thesame frequency. Alternatively, light can be emitted an any number offrequencies suitable to capture absorption data desired by one ofordinary skill in the art. Additionally, the light source can bepositioned obliquely to the sample. In other embodiments, the lightsource can be positioned at any angle relative to the sample (e.g., 10degrees, 20 degrees, 25 degrees, 30 degrees, 35 degrees, 40 degrees, 45degrees, 50 degrees, 55 degrees, 60 degrees, 65 degrees, 70 degrees, 75degrees, 80 degrees, or 90 degrees). In some embodiments, multiple lightsources can be position flanking an objective image-capturing device inan epi-illumination configuration. In such an embodiment, the obliqueangle of the light sources can be selected such that the light sourceson either side of the objective form an orthogonal angle with eachother.

In some embodiments, the plurality of cells can be any living oronce-living cells. For example, the plurality of cells can compriseblood cells, epithelial cells, cancer cells, stem cells, organoid cells,and the like. In some embodiments, the presently disclosed imagingsystems and methods can be used beyond the application of a plurality ofcells and used to image other structural features such as neurons,amyloid beta plaques, proteins, and the like.

Reference will now be made in detail to exemplary embodiments of thedisclosed technology, examples of which are illustrated in theaccompanying drawings and disclosed herein. Wherever convenient, thesame references numbers will be used throughout the drawings to refer tothe same or like parts.

FIGS. 1a-1b and 2-4 illustrate exemplary embodiments of the presentlydisclosed systems and methods of imaging cells.

In FIGS. 1a-1b , a system for imaging cells is disclosed herein. Asshown, a system 100 can be used to analyze a plurality of cells 110. Theplurality of cells 110 can comprise any living or once-living cells. Forexample, the plurality of cells can comprise blood cells, epithelialcells, cancer cells, stem cells, organoid cells, and the like. In someembodiments, the presently disclosed imaging systems and methods can beused beyond the application of a plurality of cells and used to imageother structural features such as neurons, amyloid beta plaques,proteins, and the like. The plurality of cells 110 can be imaged by anobjective image-capturing device 130. For instance, the objective cancomprise a lens or magnification and a camera for imaging. In someexamples, the objective can be a microscope lens. The system 100 canfurther comprise light sources 121 and 122. Light sources 121 and 122can be positioned on the same side of the plurality of cells 110 as theobjective 130 in what is referred to herein as an epi-illuminationconfiguration. As shown, light sources 121 and 122 can flank theobjective 130 and can be positioned obliquely to the plurality of cells110, such that the light sources 121 and 122 transmit light towards theplurality of cells 110 at oblique angles with respect to each other.Additionally, the light sources 121 and 122 can be positioned obliquelysuch that light source 121 forms an orthogonal angle with light source122. In some examples, the system can comprise a first light source 121and a second light source 122. Each light source can comprise a pair oflight-emitting devices. Alternatively, each light source can comprisetwo or more light-emitting devices. In some embodiments, each lightsource can comprise one or more light-emitting devices. Thelight-emitting devices can comprise LEDs, optical fibers, fiber opticcables, incandescent lamps, halogen light bulbs, and the like.

In FIG. 2, a method 200 for imaging cells is disclosed herein. In block210, a quantitative phase image (QPI) can be obtained along with lightfrequency absorption/transmission data for a plurality of cells beingimaged. In block 220, the QPI can be cross-correlated with a samplemodel of a desired cell. For example, if a white blood cell is thedesired cell to be imaged, a sample model of a white blood cell can beprovided and cross-correlated with the QPI. Additionally, cellsdetermined to be similar to the sample model are indicated as such. Forexample, a cell similar to the sample model can be indicated as a firstdesired cell candidate. In block 230, the light frequency absorptiondata can be used to determine outliers from the plurality of cells. Forinstance, the plurality of cells can produce a normal distribution oflight absorption at a given frequency. Cells found to lie outside athreshold standard deviation, such as 1.5 standard deviations, areindicated to be outliers. Additionally, the outliers can be indicated aspotential desired cells. For example, a cell having a light frequencyabsorption greater than 1.5 standard deviations can be indicated as asecond desired cell candidate. In block 240, the first and seconddesired cell candidates can be analyzed to determine if they are thesame cell. In other words, cells which match the sample model and areoutliers to the light absorption data are indicated as desired cells.Cells which only meet one of the criteria can be indicated as falsepositives. In block 250, the desired cells can be indicated as such.

In FIG. 3, a method 300 for imaging cells is disclosed herein. In block310, the QPI can be cross-correlated with a sample model of a desiredcell. For example, if a white blood cell is the desired cell to beimaged, a sample model of a white blood cell can be provided andcross-correlated with the QPI. Additionally, cells determined to besimilar to the sample model are indicated as such in block 320. Forexample, a cell similar to the sample model can be indicated as a firstdesired cell candidate. In block 330, the light frequency absorptiondata can be used to determine outliers from the plurality of cells. Forinstance, the plurality of cells can produce a normal distribution oflight absorption at a given frequency. Cells found to lie outside athreshold standard deviation, such as 1.5 standard deviations, areindicated to be outliers. Additionally, the outliers can be indicated aspotential desired cells. For example, a cell having a light frequencyabsorption greater than 1.5 standard deviations can be indicated as asecond desired cell candidate. In block 340, the first and seconddesired cell candidates can be analyzed to determine if they are thesame cell. In other words, cells which match the sample model and areoutliers to the light absorption data are indicated as desired cells.Cells which only meet one of the criteria can be indicated as falsepositives.

In FIG. 4, a method 400 for imaging cells is disclosed herein. In block410, the sample can be illuminated by the first and second light sourcesat a first and second frequency. In block 420, two or more illuminatedimages of the sample can be received, and the QPI can be constructedusing the methods disclosed herein. In block 430, the QPI can becross-correlated with a sample model of a desired cell. For example, ifa white blood cell is the desired cell to be imaged, a sample model of awhite blood cell can be provided and cross-correlated with the QPI.Additionally, cells determined to be similar to the sample model areindicated as such. For example, a cell similar to the sample model canbe indicated as a first desired cell candidate. In block 440, the lightfrequency absorption data can be used to determine outliers from theplurality of cells. For instance, the plurality of cells can produce anormal distribution of light absorption at a given frequency. Cellsfound to lie outside a threshold standard deviation, such as 1.5standard deviations, are indicated to be outliers. Additionally, theoutliers can be indicated as potential desired cells. For example, acell having a light frequency absorption greater than 1.5 standarddeviations can be indicated as a second desired cell candidate. In block450, the first and second desired cell candidates can be analyzed todetermine if they are the same cell. In other words, cells which matchthe sample model and are outliers to the light absorption data areindicated as desired cells. Cells which only meet one of the criteriacan be indicated as false positives. In block 460, the desired cells canbe indicated as such and labelled in an image as desired cells.

In FIG. 10, a method for constructing a Quantitative Phase Image (QPI)is described herein. In block 1010, two or more images (e.g., three ormore, four or more, five or more) from different angles relative to thesample can be subtracted from one another and normalized to produce adifferential phase contrast image. In block 1020, the angulardistribution of illumination intensity can be obtained using the methodsas described herein. In block 1030, the system transfer function can beextracted from the angular distribution. In block 1040, the systemtransfer function can be linearized to two dimensions. In block 1050,the two-dimensional transfer function can be used to construct a QPIfrom the given data using the methods as described herein.

Reference will now be made in detail to exemplary embodiments of thedisclosed technology, examples of which are illustrated in theaccompanying drawings and disclosed herein. Wherever convenient, thesame references numbers will be used throughout the drawings to refer tothe same or like parts.

EXAMPLES

The following examples are provided by way of illustration but not byway of limitation.

Example 1 Materials

In an assembly of a system for imaging cells, four LEDs (Luxeon sink-PADII) are coupled into multimode plastic fibers (Thorlabs FP1000ERT,numerical aperture (NA) 0.5, 1000 μm diameter), each using an asphericcondenser lens (Thorlabs ACL2520U-A, NA 0.6). These fibers are housed ina custom 3D printed objective adapter which holds them at the desiredincident angle and off-axis source-detector distance. From previousexperiments, the system currently uses two sets of colored LEDs, one at530 nm (green), and another 630 nm (red), selected to provide absorptionspectral information key to performing blood cell classification.Different wavelengths can be used to optimize for a specific task.Imaging was performed on an inverted microscope (Zeiss Axiovert 200)with a 60× objective, (Nikon S Plan ELWD, NA 0.7), at a resolution of0.6 pm. The LEDs illuminate the sample individually, and light isdetected with a digital camera (sCMOS pco.eddge 42LT) at 20 Hz. Theillumination and camera triggering were coordinated with custom software(National Instruments LabVIEW 2017) and a data acquisition block(National Instruments SCB-68A).

Example 2 Materials and Methods

Human blood was drawn from consenting human donors by vasopuncture intoheparinized tubes and diluted with phosphate-buffered saline (PBS) to 1%of pure blood concentration. All procedures adhered to approvedInstitutional Review Board protocols. The blood was transferred intocustom-made translucent PVC bag (InstantSystems) and placed on a glassslide over the objective to flatten the bag and reduce bowing. Theobjective lens was focused on cells from the blood bag suspension thathad settled on the inner surface of the bag, which were illuminated bythe diffuse scattering of the LED light from the fibers. Imaging fromthis constant plane provided a consistent cross-section of the cells inthe image and reduced motion artifacts. Also, the dual wavelengthconfiguration yields spectral information from the absorption images todistinguish white from red blood cells.

FIG. 7 shows representative results, where the quantitative phase imagesshow remarkable details of gross cellular morphology and sub-cellularfeatures. Here one can clearly see normal biconcave and sickled RBCs,acanthocytes, and white blood cells along with their internal contents,including the nucleus. In addition to extracting feature-richmorphology, qOBM maintains a reliable quantitative phase profile. Across section of 20 biconcave red blood cells from the displayed imageis compared with an ideal standard theoretical healthy cell profile andthe numerical simulation thereof. Again, the results show excellentagreement.

Example 3 Materials and Methods

Next, the present disclosure was used to image whole mouse brains. Allanimal experimental protocols were approved by Institutional Animal Careand Use Committee (IACUC) of the Georgia Institute of Technology. Themice (Mus musculus) used were of a C57/BL6 genetic background and were14 months old. Whole brains were dissected, briefly rinsed in phosphatebuffered saline and then placed directly on a microscope slide forimaging without staining, slicing or other alteration unless otherwisespecified in the corresponding image caption.

All images were collected at 20 Hz, limited by the signal-to-noise ratioof the image from the intensity of scattered illumination lightimpinging on the object at the focal plane. As the inverse transferfunction is produced before-hand for a given scattering medium geometry,quantitative phase images are computed and displayed in real-time,facilitating the task of finding and identifying structures andlandmarks.

FIG. 8 shows the results. Here fine structural details can clearly beobserved, such as neural cell soma with resolvable internal cellcontents, smooth muscle cells, blood vessels and nearby glial cells.From coronal sections, axons as well as cell bodies of neurons and gliawere able to be resolved. To demonstrate this optical sectioningcapability, 100 images were captured in a z-stack (0.6 μm steps),centered on a blood vessel in the cortex. The maximum intensityprojection of the 3D volume clearly shows the vessel, red blood cells,and tissue structures.

Example 4 Materials and Methods

Finally, the present disclosure was used to image cerebral organoids.Organoids are cell-derived 3D organ models that mimic the structure andcell diversity of native biological tissue and have rapidly emerged aspowerful in vitro model of diseases that are intractable or impracticalto study in humans or animal models. Organoids are typically grown andincubated in enclosed bioreactors, wherein non-invasive live imaging ofgrowth and organization of developing heterogeneous tissue couldfacilitate an understanding of, for example, complex developmentaldiseases, such as Autism Spectrum Disorder and microencephaly. FIG. 9shows a series of qOBM images of a 26-day old cerebral organoid taken atvarious depths with 10 μm increments. Notable features includeneuroblasts and immature neurons, mature neurons with sub-cellulardetail, neural progenitor cells, and the characteristic “rosette” shapeformed by neural progenitor cells that develop and grow radially. Theseimages show qOBM's unique potential to monitor the structure, growth,and health of organoids without labels.

While the present disclosure has been described in connection with aplurality of exemplary aspects, as illustrated in the various figuresand discussed above, it is understood that other similar aspects can beused or modifications and additions can be made to the described aspectsfor performing the same function of the present disclosure withoutdeviating therefrom. For example, in various aspects of the disclosure,methods and compositions were described according to aspects of thepresently disclosed subject matter. However, other equivalent methods orcomposition to these described aspects are also contemplated by theteachings herein. Therefore, the present disclosure should not belimited to any single aspect, but rather construed in breadth and scopein accordance with the appended claims.

1. A method comprising: cross-correlating a sample model of a desiredcell with a quantitative phase image of cells to compare cells of thequantitative phase image with the sample model of the desired cell;indicating a cell from the cells similar to the sample model as a firstdesired cell candidate; indicating a cell from the cells having a lightfrequency absorption outside of a threshold standard deviation from thecells as a second desired cell candidate; and determining, based on thequantitative phase image of cells and a distribution of light frequencyabsorption data for the cells, if the first desired cell candidate andthe second desired cell candidate are the same cell.
 2. The method ofclaim 1, wherein the distribution of light frequency absorption data forthe cells is obtained by: illuminating the cells with light at a firstfrequency; illuminating the cells with light at a second frequency; andreceiving two or more illuminated images of the cells.
 3. The method ofclaim 2 further comprising comparing a value of light absorbed at thefirst frequency to light absorbed at the second frequency for one ormore of the cells.
 4. The method of claim 2 further comprisingconstructing a phase gradient image by subtracting an illuminated imageat the second frequency from an illuminated image at the firstfrequency.
 5. The method of claim 2 further comprising constructing anabsorption contrast image by adding two or more illuminated imagestogether.
 6. The method of claim 2, wherein illuminating comprises:emitting light at the first frequency from a first pair of lightsources; emitting light at the second frequency from a second pair oflight sources; wherein the first and second pairs of light sources andan objective lens are on a same side of the cells; and wherein the firstand second pairs of light sources are configured to transmit lightobliquely to the cells.
 7. The method of claim 6, wherein the first andsecond pair of light sources comprise two or more light-emittingdevices.
 8. The method of claim 7, wherein the two or morelight-emitting devices comprise light-emitting diodes (LEDs).
 9. Themethod of claim 7, wherein the two or more light-emitting devicescomprise fiber optic cables.
 10. The method of claim 7, wherein a firstand a second light-emitting device are positioned flanking the objectivelens, such that each of the first and second pair of light sourcescomprise a first and a second light-emitting device on either side ofthe objective lens.
 11. The method of claim 10, wherein the first andthe second light-emitting devices on either side of the objective lensform an orthogonal angle with each other, such that each of the firstand second pair of light sources comprise a first and a secondlight-emitting device on either side of the objective lens and formingan orthogonal angle.
 12. The method of claim 1 further comprisinglabelling as a desired cell the cell when the first desired cellcandidate and the second desired cell candidate are the same cell. 13.The method of claim 1 further comprising labelling as a false positivethe cell when the first desired cell candidate and the second desiredcell candidate are not the same cell.
 14. The method of claim 1, whereinindicating the cell having the light frequency absorption outside of thethreshold standard deviation from the cells comprises: illuminating thecells with light at a first frequency; illuminating the cells with lightat a second frequency; calculating a ratio of light absorbed at thefirst frequency to light absorbed at the second frequency for one ormore of the cells; and determining which cells have a ratio of lightabsorbed outside the threshold standard deviation value from the cells.15.-17. (canceled)
 18. A method comprising: illuminating a plurality ofcells with light at a first frequency from a first light source;illuminating the plurality of cells with light at a second frequencyfrom a second light source; receiving illuminated images of theplurality of cells at an objective; constructing a quantitative phaseimage from the illuminated images; cross-correlating a sample model of adesired cell with the quantitative phase image to compare one or morecell from the plurality of cells with the desired cell; indicating atleast one cell from the plurality of cells similar to the sample modelas a first desired cell candidate; indicating at least one cell from theplurality of cells having a light frequency absorption ratio outside ofa threshold standard deviation from the plurality of cells as a seconddesired cell candidate; determining, based on the quantitative phaseimage and a distribution of light frequency absorption data, if thefirst desired cell candidate and the second desired cell candidate arethe same cell; and labelling the cell corresponding to the first and thesecond desired cell candidate as a desired cell, responsive todetermining that the first and second desired cell candidates are thesame cell. 19.-24. (canceled)
 25. A method of imaging cell samplescomprising: cross-correlating a sample model of a desired cell with aquantitative phase image to compare each cell from a plurality of cellswith the desired cell; indicating at least one cell from the pluralityof cells similar to the sample model as a first desired cell candidate;indicating at least one cell from the plurality of cells having a lightfrequency absorption outside of a threshold standard deviation from theplurality of cells as a second desired cell candidate; and determining,based on the quantitative phase image and the distribution of lightfrequency absorption data, if the first desired cell candidate and thesecond desired cell candidate are the same cell.
 26. The method of claim25 further comprising: obtaining a distribution of light frequencyabsorption data by: illuminating the plurality of cells with light at afirst frequency; illuminating the plurality of cells with light at asecond frequency; and receiving two or more illuminated image of theplurality of cells at an objective.
 27. The method of claim 26 furthercomprising calculating a ratio of light absorbed at the first frequencyto light absorbed at the second frequency for each cell from theplurality of cells.
 28. The method of claim 26 further comprisingconstructing a phase gradient image by subtracting an illuminated imageat the second frequency from an illuminated image at the firstfrequency.
 29. The method of claim 26 further comprising constructing anabsorption contrast image by adding the two or more illuminated imagestogether. 30.-35. (canceled)
 36. A system for the imaging of cells,comprising: a first and a second light source, each of the first andsecond light source comprising two or more light-emitting devices; anobjective image-capturing device; a display; a processor; and at leastone memory storing instructions that when executed by the processor,cause the system to: receive imaging data from the objectiveimage-capturing device, the imaging data comprising light frequencyabsorption data for a plurality of cells; construct, using the lightfrequency absorption data, a quantitative phase image of the pluralityof cells; cross-correlate a sample model of a desired cell with thequantitative phase image to compare each cell from the plurality ofcells with the sample model of the desired cell; indicate at least onecell from the plurality of cells similar to the sample model as a firstdesired cell candidate; indicate at least one cell from the plurality ofcells having a light frequency absorption outside of a thresholdstandard deviation from the plurality of cells as a second desired cellcandidate; and determine, based on the quantitative phase image and thedistribution of light frequency absorption data, if the first desiredcell candidate and the second desired cell candidate are the same cell.37.-55. (canceled)
 56. A system for the imaging of cells, comprising: afirst and a second light source, each of the first and second lightsource comprising two or more light-emitting devices and configured toilluminate a plurality of cells; an objective image-capturing device; aplurality of cells; a processor; and memory storing instructions that,when executed by the processor, cause the system to: receive imagingdata from the objective image-capturing device, the imaging datacomprising light frequency absorption data for a plurality of cells;construct, using the light frequency absorption data, a quantitativephase image of the plurality of cells; cross-correlate a sample model ofa desired cell with the quantitative phase image to compare each cellfrom the plurality of cells with the desired cell; indicate at least onecell from the plurality of cells similar to the sample model as a firstdesired cell candidate; indicate at least one cell from the plurality ofcells having a light frequency absorption outside of a thresholdstandard deviation from the plurality of cells as a second desired cellcandidate; wherein the first and second light sources are on the sameside of the plurality of cells as the objective and are configured totransmit light obliquely to the plurality of cells; wherein a first anda second light-emitting device from the two or more light-emittingdevices are positioned flanking the objective, such that each of thefirst and second light sources comprise a first and a secondlight-emitting device on either side of the objective; and wherein thefirst and the second light-emitting devices on either side of theobjective form an orthogonal angle with each other, such that each ofthe first and second light sources comprise a first and a secondlight-emitting device on either side of the objective and forming anorthogonal angle.